Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations21613
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 MiB
Average record size in memory168.0 B

Variable types

Numeric17
DateTime1
Categorical3

Alerts

bathrooms is highly overall correlated with bedrooms and 6 other fieldsHigh correlation
bedrooms is highly overall correlated with bathrooms and 2 other fieldsHigh correlation
floors is highly overall correlated with bathrooms and 3 other fieldsHigh correlation
grade is highly overall correlated with bathrooms and 6 other fieldsHigh correlation
long is highly overall correlated with zipcodeHigh correlation
price is highly overall correlated with grade and 3 other fieldsHigh correlation
sqft_above is highly overall correlated with bathrooms and 6 other fieldsHigh correlation
sqft_living is highly overall correlated with bathrooms and 5 other fieldsHigh correlation
sqft_living15 is highly overall correlated with bathrooms and 4 other fieldsHigh correlation
sqft_lot is highly overall correlated with sqft_lot15High correlation
sqft_lot15 is highly overall correlated with sqft_lotHigh correlation
view is highly overall correlated with waterfrontHigh correlation
waterfront is highly overall correlated with viewHigh correlation
yr_built is highly overall correlated with bathrooms and 2 other fieldsHigh correlation
zipcode is highly overall correlated with longHigh correlation
waterfront is highly imbalanced (93.6%)Imbalance
view is highly imbalanced (72.2%)Imbalance
sqft_basement has 13126 (60.7%) zerosZeros
yr_renovated has 20699 (95.8%) zerosZeros

Reproduction

Analysis started2025-10-31 06:23:32.295255
Analysis finished2025-10-31 06:23:58.946718
Duration26.65 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

Distinct21436
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5803015 × 109
Minimum1000102
Maximum9.9000002 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2025-10-31T01:23:59.007409image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1000102
5-th percentile5.1248034 × 108
Q12.1230492 × 109
median3.9049304 × 109
Q37.3089004 × 109
95-th percentile9.2973004 × 109
Maximum9.9000002 × 109
Range9.8990001 × 109
Interquartile range (IQR)5.1858513 × 109

Descriptive statistics

Standard deviation2.8765656 × 109
Coefficient of variation (CV)0.62802974
Kurtosis-1.2605419
Mean4.5803015 × 109
Median Absolute Deviation (MAD)2.4025301 × 109
Skewness0.24332855
Sum9.8994057 × 1013
Variance8.2746295 × 1018
MonotonicityNot monotonic
2025-10-31T01:23:59.114622image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7950006203
 
< 0.1%
22315000302
 
< 0.1%
12375005402
 
< 0.1%
61175018202
 
< 0.1%
78534201102
 
< 0.1%
51014056042
 
< 0.1%
93533006002
 
< 0.1%
27676021412
 
< 0.1%
51270013202
 
< 0.1%
74097002152
 
< 0.1%
Other values (21426)21592
99.9%
ValueCountFrequency (%)
10001022
< 0.1%
12000191
< 0.1%
12000211
< 0.1%
28000311
< 0.1%
36000571
< 0.1%
36000721
< 0.1%
38000081
< 0.1%
52000871
< 0.1%
62000171
< 0.1%
72000801
< 0.1%
ValueCountFrequency (%)
99000001901
< 0.1%
98950000401
< 0.1%
98423005401
< 0.1%
98423004851
< 0.1%
98423000951
< 0.1%
98423000361
< 0.1%
98393011651
< 0.1%
98393010601
< 0.1%
98393010551
< 0.1%
98393008751
< 0.1%

date
Date

Distinct372
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size169.0 KiB
Minimum2014-05-02 00:00:00
Maximum2015-05-27 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-10-31T01:23:59.221479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:59.331422image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

price
Real number (ℝ)

High correlation 

Distinct4032
Distinct (%)18.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean540088.14
Minimum75000
Maximum7700000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2025-10-31T01:23:59.446330image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum75000
5-th percentile210000
Q1321950
median450000
Q3645000
95-th percentile1156480
Maximum7700000
Range7625000
Interquartile range (IQR)323050

Descriptive statistics

Standard deviation367127.2
Coefficient of variation (CV)0.67975422
Kurtosis34.58554
Mean540088.14
Median Absolute Deviation (MAD)150000
Skewness4.0240691
Sum1.1672925 × 1010
Variance1.3478238 × 1011
MonotonicityNot monotonic
2025-10-31T01:23:59.559717image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
450000172
 
0.8%
350000172
 
0.8%
550000159
 
0.7%
500000152
 
0.7%
425000150
 
0.7%
325000148
 
0.7%
400000145
 
0.7%
375000138
 
0.6%
300000133
 
0.6%
525000131
 
0.6%
Other values (4022)20113
93.1%
ValueCountFrequency (%)
750001
< 0.1%
780001
< 0.1%
800001
< 0.1%
810001
< 0.1%
820001
< 0.1%
825001
< 0.1%
830001
< 0.1%
840001
< 0.1%
850002
< 0.1%
865001
< 0.1%
ValueCountFrequency (%)
77000001
< 0.1%
70625001
< 0.1%
68850001
< 0.1%
55700001
< 0.1%
53500001
< 0.1%
53000001
< 0.1%
51108001
< 0.1%
46680001
< 0.1%
45000001
< 0.1%
44890001
< 0.1%

bedrooms
Real number (ℝ)

High correlation 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3708416
Minimum0
Maximum33
Zeros13
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2025-10-31T01:23:59.657242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median3
Q34
95-th percentile5
Maximum33
Range33
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.93006183
Coefficient of variation (CV)0.27591383
Kurtosis49.063653
Mean3.3708416
Median Absolute Deviation (MAD)1
Skewness1.9742995
Sum72854
Variance0.86501501
MonotonicityNot monotonic
2025-10-31T01:23:59.738530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
39824
45.5%
46882
31.8%
22760
 
12.8%
51601
 
7.4%
6272
 
1.3%
1199
 
0.9%
738
 
0.2%
013
 
0.1%
813
 
0.1%
96
 
< 0.1%
Other values (3)5
 
< 0.1%
ValueCountFrequency (%)
013
 
0.1%
1199
 
0.9%
22760
 
12.8%
39824
45.5%
46882
31.8%
51601
 
7.4%
6272
 
1.3%
738
 
0.2%
813
 
0.1%
96
 
< 0.1%
ValueCountFrequency (%)
331
 
< 0.1%
111
 
< 0.1%
103
 
< 0.1%
96
 
< 0.1%
813
 
0.1%
738
 
0.2%
6272
 
1.3%
51601
 
7.4%
46882
31.8%
39824
45.5%

bathrooms
Real number (ℝ)

High correlation 

Distinct30
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1147573
Minimum0
Maximum8
Zeros10
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2025-10-31T01:23:59.828047image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11.75
median2.25
Q32.5
95-th percentile3.5
Maximum8
Range8
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation0.77016316
Coefficient of variation (CV)0.36418512
Kurtosis1.2799024
Mean2.1147573
Median Absolute Deviation (MAD)0.5
Skewness0.51110757
Sum45706.25
Variance0.59315129
MonotonicityNot monotonic
2025-10-31T01:23:59.924110image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
2.55380
24.9%
13852
17.8%
1.753048
14.1%
2.252047
 
9.5%
21930
 
8.9%
1.51446
 
6.7%
2.751185
 
5.5%
3753
 
3.5%
3.5731
 
3.4%
3.25589
 
2.7%
Other values (20)652
 
3.0%
ValueCountFrequency (%)
010
 
< 0.1%
0.54
 
< 0.1%
0.7572
 
0.3%
13852
17.8%
1.259
 
< 0.1%
1.51446
 
6.7%
1.753048
14.1%
21930
 
8.9%
2.252047
 
9.5%
2.55380
24.9%
ValueCountFrequency (%)
82
 
< 0.1%
7.751
 
< 0.1%
7.51
 
< 0.1%
6.752
 
< 0.1%
6.52
 
< 0.1%
6.252
 
< 0.1%
66
< 0.1%
5.754
 
< 0.1%
5.510
< 0.1%
5.2513
0.1%

sqft_living
Real number (ℝ)

High correlation 

Distinct1038
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2079.8997
Minimum290
Maximum13540
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2025-10-31T01:24:00.044196image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum290
5-th percentile940
Q11427
median1910
Q32550
95-th percentile3760
Maximum13540
Range13250
Interquartile range (IQR)1123

Descriptive statistics

Standard deviation918.4409
Coefficient of variation (CV)0.44157941
Kurtosis5.243093
Mean2079.8997
Median Absolute Deviation (MAD)540
Skewness1.4715554
Sum44952873
Variance843533.68
MonotonicityNot monotonic
2025-10-31T01:24:00.160171image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1300138
 
0.6%
1400135
 
0.6%
1440133
 
0.6%
1660129
 
0.6%
1800129
 
0.6%
1010129
 
0.6%
1820128
 
0.6%
1480125
 
0.6%
1720125
 
0.6%
1540124
 
0.6%
Other values (1028)20318
94.0%
ValueCountFrequency (%)
2901
< 0.1%
3701
< 0.1%
3801
< 0.1%
3841
< 0.1%
3902
< 0.1%
4101
< 0.1%
4202
< 0.1%
4301
< 0.1%
4401
< 0.1%
4601
< 0.1%
ValueCountFrequency (%)
135401
< 0.1%
120501
< 0.1%
100401
< 0.1%
98901
< 0.1%
96401
< 0.1%
92001
< 0.1%
86701
< 0.1%
80201
< 0.1%
80101
< 0.1%
80001
< 0.1%

sqft_lot
Real number (ℝ)

High correlation 

Distinct9782
Distinct (%)45.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15106.968
Minimum520
Maximum1651359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2025-10-31T01:24:00.267869image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum520
5-th percentile1800
Q15040
median7618
Q310688
95-th percentile43339.2
Maximum1651359
Range1650839
Interquartile range (IQR)5648

Descriptive statistics

Standard deviation41420.512
Coefficient of variation (CV)2.7418151
Kurtosis285.07782
Mean15106.968
Median Absolute Deviation (MAD)2618
Skewness13.060019
Sum3.2650689 × 108
Variance1.7156588 × 109
MonotonicityNot monotonic
2025-10-31T01:24:00.374435image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000358
 
1.7%
6000290
 
1.3%
4000251
 
1.2%
7200220
 
1.0%
4800120
 
0.6%
7500119
 
0.6%
4500114
 
0.5%
8400111
 
0.5%
9600109
 
0.5%
3600103
 
0.5%
Other values (9772)19818
91.7%
ValueCountFrequency (%)
5201
< 0.1%
5721
< 0.1%
6001
< 0.1%
6091
< 0.1%
6351
< 0.1%
6381
< 0.1%
6492
< 0.1%
6511
< 0.1%
6751
< 0.1%
6761
< 0.1%
ValueCountFrequency (%)
16513591
< 0.1%
11647941
< 0.1%
10742181
< 0.1%
10240681
< 0.1%
9829981
< 0.1%
9822781
< 0.1%
9204231
< 0.1%
8816541
< 0.1%
8712002
< 0.1%
8433091
< 0.1%

floors
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.494309
Minimum1
Maximum3.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2025-10-31T01:24:00.463719image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1.5
Q32
95-th percentile2
Maximum3.5
Range2.5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5399889
Coefficient of variation (CV)0.36136361
Kurtosis-0.48472294
Mean1.494309
Median Absolute Deviation (MAD)0.5
Skewness0.61617672
Sum32296.5
Variance0.29158801
MonotonicityNot monotonic
2025-10-31T01:24:00.546399image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
110680
49.4%
28241
38.1%
1.51910
 
8.8%
3613
 
2.8%
2.5161
 
0.7%
3.58
 
< 0.1%
ValueCountFrequency (%)
110680
49.4%
1.51910
 
8.8%
28241
38.1%
2.5161
 
0.7%
3613
 
2.8%
3.58
 
< 0.1%
ValueCountFrequency (%)
3.58
 
< 0.1%
3613
 
2.8%
2.5161
 
0.7%
28241
38.1%
1.51910
 
8.8%
110680
49.4%

waterfront
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size169.0 KiB
0
21450 
1
 
163

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21613
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
021450
99.2%
1163
 
0.8%

Length

2025-10-31T01:24:00.629723image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-31T01:24:00.705311image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
021450
99.2%
1163
 
0.8%

Most occurring characters

ValueCountFrequency (%)
021450
99.2%
1163
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)21613
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
021450
99.2%
1163
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)21613
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
021450
99.2%
1163
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)21613
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
021450
99.2%
1163
 
0.8%

view
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size169.0 KiB
0
19489 
2
 
963
3
 
510
1
 
332
4
 
319

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21613
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
019489
90.2%
2963
 
4.5%
3510
 
2.4%
1332
 
1.5%
4319
 
1.5%

Length

2025-10-31T01:24:00.782295image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-31T01:24:00.861324image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
019489
90.2%
2963
 
4.5%
3510
 
2.4%
1332
 
1.5%
4319
 
1.5%

Most occurring characters

ValueCountFrequency (%)
019489
90.2%
2963
 
4.5%
3510
 
2.4%
1332
 
1.5%
4319
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)21613
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
019489
90.2%
2963
 
4.5%
3510
 
2.4%
1332
 
1.5%
4319
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)21613
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
019489
90.2%
2963
 
4.5%
3510
 
2.4%
1332
 
1.5%
4319
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)21613
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
019489
90.2%
2963
 
4.5%
3510
 
2.4%
1332
 
1.5%
4319
 
1.5%

condition
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size169.0 KiB
3
14031 
4
5679 
5
1701 
2
 
172
1
 
30

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21613
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row5
5th row3

Common Values

ValueCountFrequency (%)
314031
64.9%
45679
26.3%
51701
 
7.9%
2172
 
0.8%
130
 
0.1%

Length

2025-10-31T01:24:00.945676image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-31T01:24:01.025547image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
314031
64.9%
45679
26.3%
51701
 
7.9%
2172
 
0.8%
130
 
0.1%

Most occurring characters

ValueCountFrequency (%)
314031
64.9%
45679
26.3%
51701
 
7.9%
2172
 
0.8%
130
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)21613
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
314031
64.9%
45679
26.3%
51701
 
7.9%
2172
 
0.8%
130
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)21613
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
314031
64.9%
45679
26.3%
51701
 
7.9%
2172
 
0.8%
130
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)21613
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
314031
64.9%
45679
26.3%
51701
 
7.9%
2172
 
0.8%
130
 
0.1%

grade
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6568732
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2025-10-31T01:24:01.109952image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q17
median7
Q38
95-th percentile10
Maximum13
Range12
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1754588
Coefficient of variation (CV)0.15351681
Kurtosis1.1909321
Mean7.6568732
Median Absolute Deviation (MAD)1
Skewness0.7711032
Sum165488
Variance1.3817033
MonotonicityNot monotonic
2025-10-31T01:24:01.188591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
78981
41.6%
86068
28.1%
92615
 
12.1%
62038
 
9.4%
101134
 
5.2%
11399
 
1.8%
5242
 
1.1%
1290
 
0.4%
429
 
0.1%
1313
 
0.1%
Other values (2)4
 
< 0.1%
ValueCountFrequency (%)
11
 
< 0.1%
33
 
< 0.1%
429
 
0.1%
5242
 
1.1%
62038
 
9.4%
78981
41.6%
86068
28.1%
92615
 
12.1%
101134
 
5.2%
11399
 
1.8%
ValueCountFrequency (%)
1313
 
0.1%
1290
 
0.4%
11399
 
1.8%
101134
 
5.2%
92615
 
12.1%
86068
28.1%
78981
41.6%
62038
 
9.4%
5242
 
1.1%
429
 
0.1%

sqft_above
Real number (ℝ)

High correlation 

Distinct946
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1788.3907
Minimum290
Maximum9410
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2025-10-31T01:24:01.282480image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum290
5-th percentile850
Q11190
median1560
Q32210
95-th percentile3400
Maximum9410
Range9120
Interquartile range (IQR)1020

Descriptive statistics

Standard deviation828.09098
Coefficient of variation (CV)0.46303695
Kurtosis3.4023036
Mean1788.3907
Median Absolute Deviation (MAD)450
Skewness1.4466645
Sum38652488
Variance685734.67
MonotonicityNot monotonic
2025-10-31T01:24:01.388521image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1300212
 
1.0%
1010210
 
1.0%
1200206
 
1.0%
1220192
 
0.9%
1140184
 
0.9%
1400180
 
0.8%
1060178
 
0.8%
1180177
 
0.8%
1340176
 
0.8%
1250174
 
0.8%
Other values (936)19724
91.3%
ValueCountFrequency (%)
2901
< 0.1%
3701
< 0.1%
3801
< 0.1%
3841
< 0.1%
3902
< 0.1%
4101
< 0.1%
4202
< 0.1%
4301
< 0.1%
4401
< 0.1%
4601
< 0.1%
ValueCountFrequency (%)
94101
< 0.1%
88601
< 0.1%
85701
< 0.1%
80201
< 0.1%
78801
< 0.1%
78501
< 0.1%
76801
< 0.1%
74201
< 0.1%
73201
< 0.1%
67201
< 0.1%

sqft_basement
Real number (ℝ)

Zeros 

Distinct306
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean291.50905
Minimum0
Maximum4820
Zeros13126
Zeros (%)60.7%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2025-10-31T01:24:01.493801image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3560
95-th percentile1190
Maximum4820
Range4820
Interquartile range (IQR)560

Descriptive statistics

Standard deviation442.57504
Coefficient of variation (CV)1.5182206
Kurtosis2.7155742
Mean291.50905
Median Absolute Deviation (MAD)0
Skewness1.5779651
Sum6300385
Variance195872.67
MonotonicityNot monotonic
2025-10-31T01:24:01.600474image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
013126
60.7%
600221
 
1.0%
700218
 
1.0%
500214
 
1.0%
800206
 
1.0%
400184
 
0.9%
1000149
 
0.7%
900144
 
0.7%
300142
 
0.7%
200108
 
0.5%
Other values (296)6901
31.9%
ValueCountFrequency (%)
013126
60.7%
102
 
< 0.1%
201
 
< 0.1%
404
 
< 0.1%
5011
 
0.1%
6010
 
< 0.1%
651
 
< 0.1%
707
 
< 0.1%
8020
 
0.1%
9021
 
0.1%
ValueCountFrequency (%)
48201
< 0.1%
41301
< 0.1%
35001
< 0.1%
34801
< 0.1%
32601
< 0.1%
30001
< 0.1%
28501
< 0.1%
28101
< 0.1%
27301
< 0.1%
27201
< 0.1%

yr_built
Real number (ℝ)

High correlation 

Distinct116
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1971.0051
Minimum1900
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2025-10-31T01:24:01.702893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1915
Q11951
median1975
Q31997
95-th percentile2011
Maximum2015
Range115
Interquartile range (IQR)46

Descriptive statistics

Standard deviation29.373411
Coefficient of variation (CV)0.014902757
Kurtosis-0.6574075
Mean1971.0051
Median Absolute Deviation (MAD)23
Skewness-0.4698054
Sum42599334
Variance862.79726
MonotonicityNot monotonic
2025-10-31T01:24:01.810790image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2014559
 
2.6%
2006454
 
2.1%
2005450
 
2.1%
2004433
 
2.0%
2003422
 
2.0%
2007417
 
1.9%
1977417
 
1.9%
1978387
 
1.8%
1968381
 
1.8%
2008367
 
1.7%
Other values (106)17326
80.2%
ValueCountFrequency (%)
190087
0.4%
190129
 
0.1%
190227
 
0.1%
190346
0.2%
190445
0.2%
190574
0.3%
190692
0.4%
190765
0.3%
190886
0.4%
190994
0.4%
ValueCountFrequency (%)
201538
 
0.2%
2014559
2.6%
2013201
 
0.9%
2012170
 
0.8%
2011130
 
0.6%
2010143
 
0.7%
2009230
1.1%
2008367
1.7%
2007417
1.9%
2006454
2.1%

yr_renovated
Real number (ℝ)

Zeros 

Distinct70
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.402258
Minimum0
Maximum2015
Zeros20699
Zeros (%)95.8%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2025-10-31T01:24:01.921051image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2015
Range2015
Interquartile range (IQR)0

Descriptive statistics

Standard deviation401.67924
Coefficient of variation (CV)4.7591054
Kurtosis18.701152
Mean84.402258
Median Absolute Deviation (MAD)0
Skewness4.5494934
Sum1824186
Variance161346.21
MonotonicityNot monotonic
2025-10-31T01:24:02.030118image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
020699
95.8%
201491
 
0.4%
201337
 
0.2%
200336
 
0.2%
200535
 
0.2%
200735
 
0.2%
200035
 
0.2%
200426
 
0.1%
199025
 
0.1%
200624
 
0.1%
Other values (60)570
 
2.6%
ValueCountFrequency (%)
020699
95.8%
19341
 
< 0.1%
19402
 
< 0.1%
19441
 
< 0.1%
19453
 
< 0.1%
19462
 
< 0.1%
19481
 
< 0.1%
19502
 
< 0.1%
19511
 
< 0.1%
19533
 
< 0.1%
ValueCountFrequency (%)
201516
 
0.1%
201491
0.4%
201337
0.2%
201211
 
0.1%
201113
 
0.1%
201018
 
0.1%
200922
 
0.1%
200818
 
0.1%
200735
 
0.2%
200624
 
0.1%

zipcode
Real number (ℝ)

High correlation 

Distinct70
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98077.94
Minimum98001
Maximum98199
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2025-10-31T01:24:02.141212image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum98001
5-th percentile98004
Q198033
median98065
Q398118
95-th percentile98177
Maximum98199
Range198
Interquartile range (IQR)85

Descriptive statistics

Standard deviation53.505026
Coefficient of variation (CV)0.00054553579
Kurtosis-0.85347887
Mean98077.94
Median Absolute Deviation (MAD)42
Skewness0.40566121
Sum2.1197585 × 109
Variance2862.7878
MonotonicityNot monotonic
2025-10-31T01:24:02.254811image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98103602
 
2.8%
98038590
 
2.7%
98115583
 
2.7%
98052574
 
2.7%
98117553
 
2.6%
98042548
 
2.5%
98034545
 
2.5%
98118508
 
2.4%
98023499
 
2.3%
98006498
 
2.3%
Other values (60)16113
74.6%
ValueCountFrequency (%)
98001362
1.7%
98002199
 
0.9%
98003280
1.3%
98004317
1.5%
98005168
 
0.8%
98006498
2.3%
98007141
 
0.7%
98008283
1.3%
98010100
 
0.5%
98011195
 
0.9%
ValueCountFrequency (%)
98199317
1.5%
98198280
1.3%
98188136
 
0.6%
98178262
1.2%
98177255
1.2%
98168269
1.2%
98166254
1.2%
98155446
2.1%
9814857
 
0.3%
98146288
1.3%

lat
Real number (ℝ)

Distinct5034
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.560053
Minimum47.1559
Maximum47.7776
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2025-10-31T01:24:02.369270image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum47.1559
5-th percentile47.3103
Q147.471
median47.5718
Q347.678
95-th percentile47.74964
Maximum47.7776
Range0.6217
Interquartile range (IQR)0.207

Descriptive statistics

Standard deviation0.13856371
Coefficient of variation (CV)0.0029134474
Kurtosis-0.676313
Mean47.560053
Median Absolute Deviation (MAD)0.1049
Skewness-0.48527048
Sum1027915.4
Variance0.019199902
MonotonicityNot monotonic
2025-10-31T01:24:02.475749image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.684617
 
0.1%
47.662417
 
0.1%
47.549117
 
0.1%
47.532217
 
0.1%
47.671116
 
0.1%
47.695516
 
0.1%
47.688616
 
0.1%
47.68615
 
0.1%
47.540215
 
0.1%
47.664715
 
0.1%
Other values (5024)21452
99.3%
ValueCountFrequency (%)
47.15591
< 0.1%
47.15931
< 0.1%
47.16221
< 0.1%
47.16471
< 0.1%
47.17641
< 0.1%
47.17751
< 0.1%
47.17762
< 0.1%
47.17951
< 0.1%
47.18031
< 0.1%
47.18081
< 0.1%
ValueCountFrequency (%)
47.77763
< 0.1%
47.77753
< 0.1%
47.77741
 
< 0.1%
47.77723
< 0.1%
47.77712
 
< 0.1%
47.7772
 
< 0.1%
47.77693
< 0.1%
47.77682
 
< 0.1%
47.77676
< 0.1%
47.77664
< 0.1%

long
Real number (ℝ)

High correlation 

Distinct752
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-122.2139
Minimum-122.519
Maximum-121.315
Zeros0
Zeros (%)0.0%
Negative21613
Negative (%)100.0%
Memory size169.0 KiB
2025-10-31T01:24:02.585835image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-122.519
5-th percentile-122.387
Q1-122.328
median-122.23
Q3-122.125
95-th percentile-121.979
Maximum-121.315
Range1.204
Interquartile range (IQR)0.203

Descriptive statistics

Standard deviation0.14082834
Coefficient of variation (CV)-0.0011523104
Kurtosis1.0495009
Mean-122.2139
Median Absolute Deviation (MAD)0.101
Skewness0.88505298
Sum-2641408.9
Variance0.019832622
MonotonicityNot monotonic
2025-10-31T01:24:02.695712image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-122.29116
 
0.5%
-122.3111
 
0.5%
-122.362104
 
0.5%
-122.291100
 
0.5%
-122.37299
 
0.5%
-122.36399
 
0.5%
-122.28898
 
0.5%
-122.35796
 
0.4%
-122.28495
 
0.4%
-122.36594
 
0.4%
Other values (742)20601
95.3%
ValueCountFrequency (%)
-122.5191
 
< 0.1%
-122.5151
 
< 0.1%
-122.5141
 
< 0.1%
-122.5121
 
< 0.1%
-122.5112
< 0.1%
-122.5092
< 0.1%
-122.5071
 
< 0.1%
-122.5061
 
< 0.1%
-122.5053
< 0.1%
-122.5042
< 0.1%
ValueCountFrequency (%)
-121.3152
< 0.1%
-121.3161
< 0.1%
-121.3191
< 0.1%
-121.3211
< 0.1%
-121.3251
< 0.1%
-121.3522
< 0.1%
-121.3591
< 0.1%
-121.3642
< 0.1%
-121.4021
< 0.1%
-121.4031
< 0.1%

sqft_living15
Real number (ℝ)

High correlation 

Distinct777
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1986.5525
Minimum399
Maximum6210
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2025-10-31T01:24:02.803607image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum399
5-th percentile1140
Q11490
median1840
Q32360
95-th percentile3300
Maximum6210
Range5811
Interquartile range (IQR)870

Descriptive statistics

Standard deviation685.3913
Coefficient of variation (CV)0.34501545
Kurtosis1.5970958
Mean1986.5525
Median Absolute Deviation (MAD)410
Skewness1.1081813
Sum42935359
Variance469761.24
MonotonicityNot monotonic
2025-10-31T01:24:02.905671image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1540197
 
0.9%
1440195
 
0.9%
1560192
 
0.9%
1500181
 
0.8%
1460169
 
0.8%
1580167
 
0.8%
1610166
 
0.8%
1720166
 
0.8%
1800166
 
0.8%
1620165
 
0.8%
Other values (767)19849
91.8%
ValueCountFrequency (%)
3991
 
< 0.1%
4602
 
< 0.1%
6202
 
< 0.1%
6701
 
< 0.1%
6902
 
< 0.1%
7002
 
< 0.1%
7102
 
< 0.1%
7202
 
< 0.1%
7408
< 0.1%
7503
 
< 0.1%
ValueCountFrequency (%)
62101
 
< 0.1%
61101
 
< 0.1%
57906
< 0.1%
56101
 
< 0.1%
56001
 
< 0.1%
55001
 
< 0.1%
53801
 
< 0.1%
53401
 
< 0.1%
53301
 
< 0.1%
52201
 
< 0.1%

sqft_lot15
Real number (ℝ)

High correlation 

Distinct8689
Distinct (%)40.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12768.456
Minimum651
Maximum871200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2025-10-31T01:24:03.014191image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum651
5-th percentile1999.2
Q15100
median7620
Q310083
95-th percentile37062.8
Maximum871200
Range870549
Interquartile range (IQR)4983

Descriptive statistics

Standard deviation27304.18
Coefficient of variation (CV)2.1384089
Kurtosis150.76311
Mean12768.456
Median Absolute Deviation (MAD)2505
Skewness9.5067432
Sum2.7596463 × 108
Variance7.4551823 × 108
MonotonicityNot monotonic
2025-10-31T01:24:03.116893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000427
 
2.0%
4000357
 
1.7%
6000289
 
1.3%
7200211
 
1.0%
4800145
 
0.7%
7500142
 
0.7%
8400116
 
0.5%
4500111
 
0.5%
3600111
 
0.5%
5100109
 
0.5%
Other values (8679)19595
90.7%
ValueCountFrequency (%)
6511
 
< 0.1%
6591
 
< 0.1%
6601
 
< 0.1%
7482
< 0.1%
7504
< 0.1%
7551
 
< 0.1%
7571
 
< 0.1%
7581
 
< 0.1%
7881
 
< 0.1%
7941
 
< 0.1%
ValueCountFrequency (%)
8712001
< 0.1%
8581321
< 0.1%
5606171
< 0.1%
4382131
< 0.1%
4347281
< 0.1%
4255811
< 0.1%
4229671
< 0.1%
4119621
< 0.1%
3920402
< 0.1%
3868121
< 0.1%

Interactions

2025-10-31T01:23:56.800588image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:34.639711image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:35.939221image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:37.300767image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:38.595535image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:40.159487image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:41.494429image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:42.823149image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:44.126632image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:45.747221image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:47.030719image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:48.335718image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:49.680364image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:51.481255image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:52.911119image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:54.223082image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:55.516178image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:56.871687image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:34.710861image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2025-10-31T01:23:55.132186image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:56.423775image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:58.280308image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:35.639263image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:36.988958image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:38.302289image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:39.630178image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:41.189072image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:42.512076image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:43.828430image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:45.445601image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:46.739640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:48.028376image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:49.375150image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:51.163152image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:52.582743image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:53.925964image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:55.213754image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:56.507907image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:58.355108image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:35.716839image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:37.070643image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:38.377808image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:39.935094image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:41.271048image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:42.591093image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:43.905701image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:45.521044image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:46.818913image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:48.113533image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:49.455014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:51.244156image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:52.666979image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:54.001373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:55.294049image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:56.582982image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:58.427181image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:35.792483image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:37.150135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:38.450983image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:40.010800image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:41.347171image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:42.671895image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:43.979553image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:45.599797image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:46.891324image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:48.188777image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:49.532588image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:51.325061image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:52.748201image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:54.076809image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:55.367666image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:56.656729image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:58.499620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:35.867070image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:37.227455image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:38.524998image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:40.085550image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:41.422616image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:42.748630image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:44.053014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:45.675498image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:46.963184image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:48.263992image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:49.608558image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:51.402426image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:52.830157image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:54.152584image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:55.444021image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T01:23:56.727264image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-10-31T01:24:03.202241image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
bathroomsbedroomsconditionfloorsgradeidlatlongpricesqft_abovesqft_basementsqft_livingsqft_living15sqft_lotsqft_lot15viewwaterfrontyr_builtyr_renovatedzipcode
bathrooms1.0000.5210.1300.5470.6580.0150.0080.2620.4970.6910.1920.7460.5700.0690.0630.1140.1020.5670.043-0.205
bedrooms0.5211.0000.0240.2280.3810.006-0.0210.1910.3450.5400.2300.6470.4440.2170.2020.0380.0000.1800.017-0.167
condition0.1300.0241.0000.1790.1540.0300.0580.0810.0230.1070.0940.0600.0620.0390.0130.0250.0170.2480.0670.074
floors0.5470.2280.1791.0000.5020.0190.0250.1490.3220.599-0.2720.4010.305-0.234-0.2310.0240.0220.5520.013-0.061
grade0.6580.3810.1540.5021.0000.0200.1040.2230.6580.7120.0930.7160.6630.1520.1560.1430.1180.5010.016-0.182
id0.0150.0060.0300.0190.0201.000-0.0040.0070.0040.0040.0010.002-0.000-0.117-0.1150.0290.0060.027-0.017-0.005
lat0.008-0.0210.0580.0250.104-0.0041.000-0.1430.456-0.0260.1160.0310.028-0.122-0.1170.0680.034-0.1260.0250.250
long0.2620.1910.0810.1490.2230.007-0.1431.0000.0640.385-0.2000.2850.3800.3710.3730.0850.0960.413-0.075-0.577
price0.4970.3450.0230.3220.6580.0040.4560.0641.0000.5420.2520.6440.5720.0750.0630.2080.3200.1020.102-0.009
sqft_above0.6910.5400.1070.5990.7120.004-0.0260.3850.5421.000-0.1660.8440.6970.2720.2540.0890.0830.4720.031-0.279
sqft_basement0.1920.2300.094-0.2720.0930.0010.116-0.2000.252-0.1661.0000.3280.1300.0370.0310.1590.134-0.1780.0630.115
sqft_living0.7460.6470.0600.4010.7160.0020.0310.2850.6440.8440.3281.0000.7470.3040.2840.1490.1400.3520.053-0.207
sqft_living150.5700.4440.0620.3050.663-0.0000.0280.3800.5720.6970.1300.7471.0000.3600.3660.1470.0890.336-0.006-0.287
sqft_lot0.0690.2170.039-0.2340.152-0.117-0.1220.3710.0750.2720.0370.3040.3601.0000.9220.0400.014-0.0380.009-0.319
sqft_lot150.0630.2020.013-0.2310.156-0.115-0.1170.3730.0630.2540.0310.2840.3660.9221.0000.0350.000-0.0160.009-0.326
view0.1140.0380.0250.0240.1430.0290.0680.0850.2080.0890.1590.1490.1470.0400.0351.0000.5920.0410.1090.074
waterfront0.1020.0000.0170.0220.1180.0060.0340.0960.3200.0830.1340.1400.0890.0140.0000.5921.0000.0320.0920.079
yr_built0.5670.1800.2480.5520.5010.027-0.1260.4130.1020.472-0.1780.3520.336-0.038-0.0160.0410.0321.000-0.215-0.317
yr_renovated0.0430.0170.0670.0130.016-0.0170.025-0.0750.1020.0310.0630.053-0.0060.0090.0090.1090.092-0.2151.0000.062
zipcode-0.205-0.1670.074-0.061-0.182-0.0050.250-0.577-0.009-0.2790.115-0.207-0.287-0.319-0.3260.0740.079-0.3170.0621.000

Missing values

2025-10-31T01:23:58.608487image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-31T01:23:58.834322image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

iddatepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtyr_renovatedzipcodelatlongsqft_living15sqft_lot15
0712930052020141013T00000022190031.00118056501.0003711800195509817847.5112-122.25713405650
1641410019220141209T00000053800032.25257072422.000372170400195119919812547.7210-122.31916907639
2563150040020150225T00000018000021.00770100001.000367700193309802847.7379-122.23327208062
3248720087520141209T00000060400043.00196050001.000571050910196509813647.5208-122.39313605000
4195440051020150218T00000051000032.00168080801.0003816800198709807447.6168-122.04518007503
5723755031020140512T000000122500044.5054201019301.00031138901530200109805347.6561-122.0054760101930
6132140006020140627T00000025750032.25171568192.0003717150199509800347.3097-122.32722386819
7200800027020150115T00000029185031.50106097111.0003710600196309819847.4095-122.31516509711
8241460012620150415T00000022950031.00178074701.000371050730196009814647.5123-122.33717808113
9379350016020150312T00000032300032.50189065602.0003718900200309803847.3684-122.03123907570
iddatepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtyr_renovatedzipcodelatlongsqft_living15sqft_lot15
21603785214004020140825T00000050725032.50227055362.0003822700200309806547.5389-121.88122705731
21604983420136720150126T00000042900032.00149011263.0003814900201409814447.5699-122.28814001230
21605344890021020141014T00000061068542.50252060232.0003925200201409805647.5137-122.16725206023
21606793600042920150326T000000100750043.50351072002.000392600910200909813647.5537-122.39820506200
21607299780002120150219T00000047500032.50131012942.000381180130200809811647.5773-122.40913301265
2160826300001820140521T00000036000032.50153011313.0003815300200909810347.6993-122.34615301509
21609660006012020150223T00000040000042.50231058132.0003823100201409814647.5107-122.36218307200
21610152330014120140623T00000040210120.75102013502.0003710200200909814447.5944-122.29910202007
2161129131010020150116T00000040000032.50160023882.0003816000200409802747.5345-122.06914101287
21612152330015720141015T00000032500020.75102010762.0003710200200809814447.5941-122.29910201357